Individual identifying device

ABSTRACT

An imaging unit, an extraction unit, a feature amount pair generation unit, and an imaging parameter adjustment unit are included. The imaging unit acquires images obtained by imaging each of N (N≥3) types of objects a plurality of times by setting a value of a specific imaging parameter, among a plurality of types of imaging parameters, as a certain candidate value and changing a value of the remaining imaging parameter. The extraction unit extracts a feature amount from each of the images. The feature amount pair generation unit generates, as a first feature amount pair for each of the N types of objects, a feature amount pair in which two feature amounts constituting the feature amount pair are extracted from images of objects of the same type, and generates, as a second feature amount pair for every combination of the N types of objects, a feature amount pair in which two feature amounts constituting the feature amount pair are extracted from a images of objects of the different types. The imaging parameter adjustment unit generates a first distribution that is a distribution of collation scores of the first feature amount pairs, generates a second distribution that is a distribution of collation scores of the second feature amount pairs, and on the basis of a degree of separation between the first distribution and the second distribution, determines the propriety of adopting the candidate value.

TECHNICAL FIELD

The present invention relates to an individual identifying device, anindividual identifying method, an individual registration device, anindividual registration method, an individual collation device, anindividual collation method, and a program.

BACKGROUND ART

Attempts are made to utilize individual difference in fine unevenpatterns formed on surfaces of objects for authentication andverification.

For example, Patent Document 1 describes use of an uneven patternprovided by a satin pattern formed on a surface of an object, forauthentication and verification of the object. Patent Document 1describes an imaging aid, an imaging device, and an imaging method forcapturing such fine uneven patterns with good contrast.

Specifically, an imaging aid described in Patent Document 1 aidsphotographing of a predetermined area of a surface of an object havingfine irregularities and strong specular reflection. The imaging aidincludes a light source unit that emits light, and a covering portion.The covering portion has a shape that covers a predetermined area of asurface of an object. In the covering portion, a partial surfacecorresponding to the inside of the range of a predetermined angle θ fromthe normal direction directly facing the predetermined area is black,and the remaining surface corresponding to the range of an angle φ is alight source area surface that diffuses and emits light emitted from thelight source unit. The covering portion also has a mechanism forperforming image capturing of a predetermined area from the normaldirection, in the black area. Further, the covering portion isconfigured such that the height of the side surface is adjustable by ascrew mechanism. When the height of the side surface of the coveringportion is changed, the range of the angle φ and the range of the angleθ vary. When the range of the angle φ and the range of the angle θ arechanged, the light and dark contrast in the uneven portion is changed.In Patent Document 1, it is configured to obtain a captured image inwhich the light and dark contrast of the uneven portion is emphasized,by adjusting the range of the angle θ and the range of the angle φ.

On the other hand, Patent Document 2 describes an image processingdevice that identifies objects that are similar but different. The imageprocessing device includes an imaging means, a management means, firstto third specifying means, and an output means. The imaging meansacquires a plurality of images captured on the basis of a plurality oftypes of imaging conditions respectively. The management means extractsa feature amount from each of the captured images, and manages a set ofthe extracted feature amounts as a feature amount cluster, inassociation with the imaging condition of the captured image from whichthe feature amount is extracted. The first specifying means specifies,from a feature amount cluster group managed by the management means, asecond feature amount cluster including a feature amount similar to thefeature amount in a first feature amount cluster managed by themanagement means. The second specifying means specifies the featureamounts associated with the same imaging condition from the firstfeature amount cluster and the second feature amount cluster, andobtains a distance between the specified feature amounts. The thirdspecifying means specifies a largest distance from the distancesobtained for a plurality of types of imaging conditions by the secondspecifying means. The output means outputs, among a plurality of typesof the imaging conditions, the imaging condition from which the largestdistance specified by the third specifying means is obtained, as animaging condition by which the first feature amount cluster and thesecond feature amount cluster can be discriminated from each other.

Then, in Patent Document 2, an object is identified using the featureamount cluster learned and the imaging condition set by the processingdescribed above. Specifically, first, a feature amount is extracted froman image in which an object is shown, and a feature amount having asmallest distance from the extracted feature amount is selected as aprimary identifying feature amount from the feature amount cluster.Then, when no imaging condition is applied to the primary identifyingfeature, the identifying process ends. On the other hand, when animaging condition is applied to the primary identifying feature amount,the imaging condition is changed to the applied one, the object isimaged again, and a feature amount is extracted from the image. Then,secondary identification is performed on the basis of the extractedfeature amount and the distance between the primary identifying featureamount and a feature amount close to the primary identifying featureamount.

Patent Document 1: WO 2014/021449 A

Patent Document 2: JP 2011-096135 A

SUMMARY

To utilize a fine uneven pattern formed on a surface of an object forauthentication and collation of the object, it is necessary to capturean image from which a fine uneven pattern unique to the object can beread stably. In Patent Document 1, although an imaging aid for capturinga fine uneven pattern on a surface of an object with good contrast isproposed, no attention is paid to stable reading of a fine unevenpattern unique to the object among fine uneven patterns on the surfaceof the object. For example, on the head surfaces of bolts or the likethat are mass-produced using a certain manufacturing mold, there is afine uneven pattern unique to each of the products, in addition to thefine uneven patterns unique to the manufacturing mold. When performingauthentication and collation of objects, it is more important thatreading of a fine uneven pattern unique to each of the products can beperformed stably, than reading of fine uneven patterns unique to themanufacturing mold.

On the other hand, in Patent Document 2, imaging conditions useful fordiscriminating similar objects are determined on the basis of thedistance between the features extracted from the images obtained byimaging the objects. Therefore, by using the technique described inPatent Document 2, it is possible to determine an imaging condition bywhich a pattern useful for discriminating two similar objects, among thepatterns on the surfaces of the objects, can be read. However, theimaging condition determined in Patent Document 2 is an imagingcondition useful for identifying two objects that are similar to eachother. Therefore, with the determined imaging condition, it is difficultto identify each of three or more objects that are similar to oneanother.

The present invention is to provide an individual identifying devicethat solves the problem described above, that is, a problem that it isdifficult to determine an imaging condition useful for identifying threeor more types of objects that are similar to one another.

An individual identifying device according to an exemplary aspect of thepresent invention includes an imaging unit that acquires a plurality ofimages obtained by imaging each of N (N≥3) types of objects a pluralityof times by setting a value of a specific imaging parameter, among aplurality of types of imaging parameters, as a certain candidate valueand changing a value of a remaining imaging parameter;

an extraction unit that extracts a feature amount from each of theplurality of the images;

a feature amount pair generation unit that generates, as a first featureamount pair for each of the N types of objects, a feature amount pair inwhich two feature amounts constituting the feature amount pair areextracted from a plurality of images of objects of the same type, andgenerates, as a second feature amount pair for every combination of theN types of objects, a feature amount pair in which two feature amountsconstituting the feature amount pair are extracted from a plurality ofimages of objects of different types; and an imaging parameteradjustment unit that generates a first distribution that is adistribution of collation scores of the first feature amount pairs,generates a second distribution that is a distribution of collationscores of the second feature amount pairs, and on the basis of a degreeof separation between the first distribution and the seconddistribution, determines the propriety of adopting the candidate value.

An individual identifying method according to another exemplary aspectof the present invention includes

acquiring a plurality of images obtained by imaging each of N (N≥3)types of objects a plurality of times by setting a value of a specificimaging parameter, among a plurality of types of imaging parameters, asa certain candidate value and changing a value of the remaining imagingparameter;

extracting a feature amount from each of the plurality of the images;

generating, as a first feature amount pair for each of the N types ofobjects, a feature amount pair in which two feature amounts constitutingthe feature amount pair are extracted from a plurality of images ofobjects of the same type, and generating, as a second feature amountpair for every combination of the N types of objects, a feature amountpair in which two feature amounts constituting the feature amount pairare extracted from a plurality of images of objects of different types;and

generating a first distribution that is a distribution of collationscores of the first feature amount pairs, generating a seconddistribution that is a distribution of collation scores of the secondfeature amount pairs, and on the basis of a degree of separation betweenthe first distribution and the second distribution, determining thepropriety of adopting the candidate value, and on the basis of a degreeof separation between the first distribution and the seconddistribution, determining the propriety of adopting the candidate value.

An individual registration device according to another exemplary aspectof the present invention includes

an imaging parameter storage unit that stores a value of an imagingparameter useful for identifying three or more types of objects that aresimilar to one another;

an imaging condition control unit that sets an imaging conditiondetermined by the value of the imaging parameter;

an imaging unit that acquires an image of an object under the imagingcondition; and

an extraction unit that extracts a feature amount from the image, andregisters the feature amount in the storage unit.

An individual registration method according to another exemplary aspectof the present invention includes

setting an imaging condition determined by a value of an imagingparameter useful for identifying three or more types of objects that aresimilar to one another;

acquiring an image of an object under the imaging condition; and

extracting a feature amount from the image, and registering the featureamount in a storage unit.

An individual collation device according to another exemplary aspect ofthe present invention includes

an imaging parameter storage unit that stores a value of an imagingparameter useful for identifying three or more types of objects that aresimilar to one another;

an imaging condition control unit that sets an imaging conditiondetermined by the value of the imaging parameter;

an imaging unit that acquires an image of an object under the imagingcondition;

an extraction unit that extracts a feature amount from the image; and

a collation unit that collates the feature amount with a registeredfeature amount stored in a storage unit.

An individual collation method according to another exemplary aspect ofthe present invention includes

setting an imaging condition determined by a value of an imagingparameter useful for identifying three or more types of objects that aresimilar to one another;

acquiring an image of an object under the imaging condition;

extracting a feature amount from the image; and

collating the feature amount with a registered feature amount stored ina storage unit.

A program according to another exemplary aspect of the present inventioncauses a computer to function as

an imaging unit that acquires a plurality of images obtained by imagingeach of N (N≥3) types of objects a plurality of times by setting a valueof a specific imaging parameter, among a plurality of types of imagingparameters, as a certain candidate value and changing a value of theremaining imaging parameter;

an extraction unit that extracts a feature amount from each of theplurality of the images;

a feature amount pair generation unit that generates, as a first featureamount pair for each of the N types of objects, a feature amount pair inwhich two feature amounts constituting the feature amount pair areextracted from a plurality of images of objects of the same type, andgenerates, as a second feature amount pair for every combination of theN types of objects, a feature amount pair in which two feature amountsconstituting the feature amount pair are extracted from a plurality ofimages of objects of different types; and

an imaging parameter adjustment unit that generates a first distributionthat is a distribution of collation scores of the first feature amountpairs, generates a second distribution that is a distribution ofcollation scores of the second feature amount pairs, and on the basis ofa degree of separation between the first distribution and the seconddistribution, determines the propriety of adopting the candidate value.

Since the present invention is configured as described above, thepresent invention can determine an imaging condition useful foridentifying three or more types of objects that are similar to oneanother.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram of an individual identifying device accordingto a first exemplary embodiment of the present invention.

FIG. 2 is a block diagram illustrating exemplary hardware of theindividual identifying device according to the first exemplaryembodiment of the present invention.

FIG. 3 is an operational flow of an exemplary process of determining avalue of a specific imaging parameter in the individual identifyingdevice according to the first embodiment of the present invention.

FIG. 4 is a flowchart illustrating an exemplary process of determining avalue of a specific imaging parameter in the individual identifyingdevice according to the first exemplary embodiment of the presentinvention.

FIG. 5 is a diagram illustrating exemplary contents of an image storageunit of the individual identifying device according to the firstexemplary embodiment of the present invention.

FIG. 6 is a diagram illustrating exemplary contents of a feature amountstorage unit of the individual identifying device according to the firstexemplary embodiment of the present invention.

FIG. 7 is a diagram illustrating exemplary contents of a first featureamount pair storage unit of the individual identifying device accordingto the first exemplary embodiment of the present invention.

FIG. 8 is a diagram illustrating exemplary contents of a second featureamount pair storage unit of the individual identifying device accordingto the first exemplary embodiment of the present invention.

FIG. 9 is a diagram illustrating exemplary contents of a distributionstorage unit of the individual identifying device according to the firstexemplary embodiment of the present invention.

FIG. 10 is a diagram illustrating an exemplary scale of the degree ofseparation used in the individual identifying device according to thefirst exemplary embodiment of the present invention.

FIG. 11 is a diagram illustrating another exemplary scale of the degreeof separation used in the individual identifying device according to thefirst exemplary embodiment of the present invention.

FIG. 12A is a diagram illustrating still another exemplary scale of thedegree of separation used in the individual identifying device accordingto the first exemplary embodiment of the present invention.

FIG. 12B is a diagram illustrating still another exemplary scale of thedegree of separation used in the individual identifying device accordingto the first exemplary embodiment of the present invention.

FIG. 13 is a flowchart illustrating another exemplary process ofdetermining a value of a specific imaging parameter in the individualidentifying device according to the first exemplary embodiment of thepresent invention.

FIG. 14 is an operational flow of an example of individual registrationin the individual identifying device according to the first exemplaryembodiment of the present invention.

FIG. 15 is a flowchart illustrating an example of individualregistration in the individual identifying device according to the firstexemplary embodiment of the present invention.

FIG. 16 is a diagram illustrating exemplary contents of individualregistration information stored in a feature amount storage unit of theindividual identifying device according to the first exemplaryembodiment of the present invention.

FIG. 17 is an operational flow of individual identification andindividual collation in the individual identifying device according tothe first exemplary embodiment of the present invention.

FIG. 18 is a flowchart illustrating an exemplary process of individualidentification and individual collation in the individual identifyingdevice according to the first exemplary embodiment of the presentinvention.

FIG. 19 is a side view illustrating an exemplary configuration of animaging unit having a coaxial vertical illuminator to be used in theindividual identifying device according to the first exemplaryembodiment of the present invention.

FIG. 20 is a diagram illustrating an example of imaging a surface,directly facing a camera, to be white (high brightness) by an imagingunit having a coaxial vertical illuminator to be used in the individualidentification apparatus according to the first exemplary embodiment ofthe present invention.

FIG. 21A is a diagram illustrating a state where light incident angle ina camera confronting direction can be adjusted by adjusting the distancefrom the coaxial vertical illuminator, to be used in the individualidentifying device according to the first exemplary embodiment of thepresent invention, to a surface of an object.

FIG. 21B is a diagram illustrating a state where light incident angle ina camera confronting direction can be adjusted by adjusting the distancefrom the coaxial vertical illuminator, to be used in the individualidentifying device according to the first exemplary embodiment of thepresent invention, to a surface of an object.

FIG. 22 is a side view illustrating an exemplary configuration of animaging unit having a coaxial vertical illuminator of a barrel type tobe used in the individual identifying device according to the firstexemplary embodiment of the present invention.

FIG. 23 is a side view illustrating an exemplary configuration of animaging unit having a ring illuminator to be used in the individualidentifying device according to the first exemplary embodiment of thepresent invention.

FIG. 24 is a diagram illustrating an example of imaging a surface,directly facing a camera, to be black (low brightness) by an imagingunit having a ring illuminator to be used in the individual identifyingdevice according to the first exemplary embodiment of the presentinvention.

FIG. 25 is a side view illustrating an exemplary configuration of animaging unit having a dome illuminator to be used in the individualidentifying device according to the first exemplary embodiment of thepresent invention.

FIG. 26 is a block diagram of an individual identifying device accordingto a second exemplary embodiment of the present invention.

FIG. 27 is a flowchart illustrating an exemplary operation of individualregistration in the individual identifying device according to thesecond exemplary embodiment of the present invention.

FIG. 28 is a flowchart illustrating an exemplary operation of individualidentification and individual collation in the individual identifyingdevice according to the second exemplary embodiment of the presentinvention.

FIG. 29 is a block diagram of an individual identifying device accordingto a third exemplary embodiment of the present invention.

FIG. 30 is a flowchart illustrating an exemplary operation of theindividual identifying device according to the third exemplaryembodiment of the present invention.

FIG. 31 is a block diagram of an individual registration deviceaccording to a fourth exemplary embodiment of the present invention.

FIG. 32 is a flowchart illustrating an exemplary operation of theindividual registration device according to the fourth exemplaryembodiment of the present invention.

FIG. 33 is a block diagram of an individual collation device accordingto the fourth exemplary embodiment of the present invention.

FIG. 34 is a flowchart illustrating an exemplary operation of theindividual collation device according to the fourth exemplary embodimentof the present invention.

EXEMPLARY EMBODIMENTS First Exemplary Embodiment

FIG. 1 is a block diagram of an individual identifying device accordingto the present embodiment. An individual identifying device 100according to the present embodiment includes an imaging unit 101, animaging condition control unit 102, an image storage unit 103, a featureamount extraction unit 104, a feature amount storage unit 105, a featureamount pair generation unit 106, a first feature amount pair storageunit 107, a second feature amount pair storage unit 108, a scorecalculation unit 109, a distribution generation unit 110, a distributionstorage unit 111, an imaging parameter determination unit 113, animaging parameter storage unit 114, a determination unit 115, and aninformation presentation unit 116.

The imaging unit 101 has a function of capturing, by a camera, images ofuneven patterns on a surface of an object to be managed, on the basis ofthe imaging conditions having been set. Uneven patterns on a surface ofan object may be a satin pattern formed on a surface of a product thatis an object, fine irregularities, patterns, or the like that arespontaneously generated in the manufacturing process.

The imaging condition control unit 102 has a function of controlling animaging condition of the imaging unit 101. The imaging condition controlunit 102 sets an imaging condition according to a combination of valuesof a plurality of types of imaging parameters. The imaging parametersinclude a plurality of types such as a distance between an object and acamera, conditions for illumination emitted to the object (emittingdirection of illumination light to the object, wavelength, intensity,and the like), an angle of view of the camera, relative posture betweenthe object and the camera, and resolution of the camera. The imagingcondition control unit 102 changes the imaging condition of the imagingunit 101 by changing the combination of values of the imagingparameters.

The image storage unit 103 has a function of storing an image of anuneven pattern on the surface of the object obtained by imaging by theimaging unit 101.

The feature amount extraction unit 104 has a function of extracting afeature amount from an image of an uneven pattern on a surface of theobject stored in the image storage unit 103. For example, the featureamount extraction unit 104 extracts characteristic points (featurepoints) existing at edges or corners from an image, by the action of adifferential filter (sobel or the like) on the two-dimensional luminancedistribution of the image of the uneven pattern. Then, the featureamount extraction unit 104 calculates, from an extracted feature pointand a pixel value of a pixel in the vicinity thereof, the feature amountrelating to the feature point (local feature amount). As the localfeature amount, a method of assigning an orientation (direction) foreach feature point on the basis of the gradient or the gradientdirection of the distribution of pixel values in a two-dimensionalarray, such as Scale Invariant Feature Transform (SIFT) or Binary RobustIndependent Elementary Features (BRIEF) can be used, for example.However, it is not limited thereto. The image itself of the unevenpattern may be used as a feature amount.

The feature amount storage unit 105 has a function of storing thefeature amount extracted by the feature amount extraction unit 104.

The feature amount pair generation unit 106 has a function of generatinga first feature amount pair for each object from a plurality of featureamounts of a plurality of objects stored in the feature amount storageunit 105. Here, the first feature amount pair means a pair in which twofeatures constituting the pair are extracted from a plurality of imagesof objects of the same type (imaging conditions of the images aredifferent). The feature amount pair generation unit 106 also has afunction of generating a second feature amount pair for everycombination of the objects from a plurality of feature amounts of theobjects stored in the feature amount storage unit 105. Here, the secondfeature amount pair means a pair in which two feature amountsconstituting the pair are extracted from a plurality of images ofobjects of different types (imaging conditions of the images may be thesame or different).

The first feature amount pair storage unit 107 has a function of storinga plurality of first feature amount pairs generated by the featureamount pair generation unit 106. The second feature amount pair storageunit 108 has a function of storing a plurality of second feature amountpairs generated by the feature amount pair generation unit 106.

The score calculation unit 109 has a function of calculating acorrelation between two feature amounts, and calculating a collationscore representing the degree of similarity between the two featureamounts. Here, feature amount pairs of two feature amounts for which ascore is calculated include the first feature amount pair, the secondfeature amount pair, and a pair configured of a feature amount that is atarget of identification/collation extracted by the feature amountextraction unit 104 and a reference feature amount stored in the featureamount storage unit 105. For example, the score calculation unit 109calculates a score by using the number of local feature amountscorresponding to both feature amounts. Alternatively, the scorecalculation unit 109 calculates a score from the Hamming distancebetween codes representing the local feature amounts of the two, forexample. The score may be a value that increases as the two featureamounts are similar, that is, the distance between the two featureamounts is smaller, or may be a value that decreases in contrast.However, the method of calculating the score is not limited to theexample described above.

The distribution generation unit 110 has a function of generating afirst distribution that is a distribution of collation scores of aplurality of first feature amount pairs. The distribution generationunit 110 also has a function of generating a second distribution that isa distribution of collation scores of a plurality of second featureamount pairs. Here, each of the first distribution and the seconddistribution is information in which a range of scores are divided intosome sections and the number of the first feature amount pairs and thenumber of the second feature pairs appearing in each section areexpressed in the form of a table form or a graph.

The distribution storage unit 111 has a function of storing the firstdistribution and the second distribution generated by the distributiongeneration unit 110.

The imaging parameter determination unit 113 has a function ofcalculating the degree of separation between the first distribution andthe second distribution stored in the distribution storage unit 111. Theimaging parameter determination unit 113 also has a function ofdetermining a value of an imaging parameter to be used, on the basis ofthe calculated degree of separation.

The imaging parameter storage unit 114 has a function of storing thevalue of the imaging parameter determined by the imaging parameterdetermination unit 113.

The determination unit 115 has a function of generating a determinationresult of identification and collation, on the basis of the scorecalculated by the score calculation unit 109 for a pair configured of afeature amount that is a target of identification and collationextracted by the feature amount extraction unit 104 and a referencefeature amount stored in the feature amount storage unit 105.

The information presentation unit 116 has a function of presentingobject management information on the basis of a determination result ofthe determination unit 115.

The individual identifying device 100 can be implemented by aninformation processing device 150 such as a personal computer or a smartphone including an photographing unit 151 such as a camera, an operationinput unit 152 such as a keyboard and a mouse, a screen display unit 153such as a liquid crystal display, a communication interface unit 154, astorage unit 155 such as a memory and a hard disk, and at least onearithmetic processing unit 156 such as a microprocessor, and a program157, as illustrated in FIG. 2, for example.

The program 157 is read from an external computer-readable storagemedium into the memory when the information processing device 150 isstarted, and controls operation of the arithmetic processing unit 156 toimplement, on the arithmetic processing unit 156, functional means suchas the imaging unit 101, the imaging condition control unit 102, theimage storage unit 103, the feature amount extraction unit 104, thefeature amount storage unit 105, the feature amount pair generation unit106, the first feature amount pair storage unit 107, the second featureamount pair storage unit 108, the score calculation unit 109, thedistribution generation unit 110, the distribution storage unit 111, theimaging parameter determination unit 113, the imaging parameter storageunit 114, the determination unit 115, and the information presentationunit 116.

Next, operation of the individual identifying device 100 according tothe present embodiment will be described with reference to the drawings.Operation of the individual identifying device 100 is roughly dividedinto three as described below.

(a) Operation of pre-processing to determine a value of a specificimaging parameter(b) Operation of individual registration(c) Operation of individual identification and individual collation

[Pre-Processing: Process of Determining Value of Specific ImagingParameter]

First, as a preliminary step, operation of a process of determining avalue of a specific imaging parameter will be described.

FIGS. 3 and 4 illustrate an operational flow and a flowchart of anexemplary process of determining a value of a specific imagingparameter.

First, assuming that specific one or more imaging parameters, among aplurality of types of imaging parameters, are imaging parameters A, andthat the imaging parameters other than the imaging parameter A areimaging parameters B, the imaging condition control unit 102 generatescandidate values a1, a2, . . . , an for the values of the imagingparameters A and candidate values b1, b2, . . . bm for the values of theimaging parameters B (step S101). For example, in the case of imaging anuneven pattern on a surface of an object by using an imaging aiddescribed in Patent Document 1, the height of a side surface of thecovering portion is used as the imaging parameter A, and as candidatevalues thereof, some values of heights a1, a2, . . . , an are generated.Further, for example, the object posture at the time of imaging is usedas the imaging parameter B, and as candidate values thereof, some valuesof postures b1, b2, . . . , bm are generated. In the above example, theimaging parameter A is configured of one type of parameter. However, itmay be configured of a plurality of types of parameters. For example, inthe case of imaging an uneven pattern on the surface of an object usingthe imaging aid described in Patent Document 1, two parameters, namelythe height of a side surface of the covering portion and the intensityof illumination, are used as the imaging parameters A, and somecombinations of the height and the intensity of illumination aregenerated as candidate values a1, a2, . . . , an. Similarly, the imagingparameters B may be configured of a plurality of types of parameters.

Then, the imaging condition control unit 102 sets a variable i, forselecting a candidate value of the imaging parameter A, to 1 (stepS101), and selects a candidate value a1 as the imaging parameter A (stepS102). Then, the imaging condition control unit 102 sets 1 to thevariable i for selecting a candidate value of the imaging parameter B(step S103), and selects a candidate value b1 as the imaging parameter B(step S104). Then, the imaging condition control unit 102 sets animaging condition determined by the candidate value a1 and the candidatevalue b1 selected, to the imaging unit 101 (step S105). Setting of theimaging condition to the imaging unit 101 may be automated, or may beset manually by the user by displaying the imaging condition on thescreen display unit 153. Next, the imaging unit 101 captures an image ofeach of previously prepared N (N≥3) pieces of sample objects at leastonce, and stores it in the image storage unit 103 (step S106). Throughthe above-described operation, in the image storage unit 103, N piecesof images G111, G211, . . . , and GN11, illustrated in FIG. 5, forexample, are stored in association with the IDs and the imagingconditions (a1, b1) of the sample objects.

Then, the feature amount extraction unit 104 reads, from the imagestorage unit 103, the images of the N pieces of sample objects capturedby being imaged under the imaging conditions of the candidate values a1and b1, extracts a feature amount from each image, and stores it in thefeature amount storage unit 105 (step S107). Thereby, in the featureamount storage unit 105, N pieces of feature amounts V111, V211, . . . ,VN11, illustrated in FIG. 6, for example, are stored in association withthe IDs and the imaging conditions (a1, b1) of the sample objects.

Then, the imaging condition control unit 102 increments the variable j(step S108), and when the variable j becomes larger than m, the imagingcondition control unit 102 returns to step S104 and repeats a processsimilar to that described above. Thereby, the images of the N pieces ofsample objects that are the same as above are captured under the imagingconditions of the candidate values a1 and b2, and a feature amount isextracted from each of the images. A similar operation is repeated untilthe variable j becomes larger than m. Thereby, each of the N pieces ofsample objects are imaged a plurality of times by fixing the value ofthe imaging parameter A to a1 and sequentially changing the value of theimaging parameter B from b1, b2, . . . bm, and also, a feature amount isextracted from each of the images. The images G111, G211, . . . , GN11,G112, G212, . . . , GN12, . . . , G11 m, G21 m, . . . , GN1 m,illustrated in FIG. 5, and the feature amounts V111, V211, . . . , VN11,V112, V212, . . . , VN12, . . . , V11 m, V21 m, . . . , VN1 m,illustrated in FIG. 6, are images and feature amounts obtained byimaging performed as described above

Next, the feature amount pair generation unit 106 reads, from thefeature amount storage unit 105, the feature amounts V111, V211, . . . ,VN11, V112, V212, . . . , VN12, . . . , V11 m, and V21 m, generates thefirst feature amount pair and the second feature amount pair, and storesthem in the first feature amount pair storage unit 107 and the secondfeature amount pair storage unit 108 (step S110). Thereby, in the firstfeature amount pair storage unit 107, the first feature amount pairsconfigured of a combination of two selected from m pieces of the featureamounts (V111, V112, . . . , V11 m), a combination of two selected fromm pieces of the feature amounts (V211, V212, . . . , V21 m), and acombination of two selected from m pieces of feature amounts (VN11,VN12, . . . , VN1 m), as illustrated in FIG. 7, for example, are storedin association with the imaging parameter A=a1. Also, in the secondfeature amount pair storage unit 108, the second feature amount pairsconfigured of a combination of the feature amount V11 x (x=1, 2, . . . ,m) and a feature amount Viyy (i≠1, y is arbitrary), a combination of afeature amount V21 x (x=1, 2, . . . , m) and the feature amount Viyy(i≠1, y is arbitrary), . . . , and a combination of a feature amount VN1x (x=1, 2, . . . , m) and the feature amount Viyy (i≠1, y is arbitrary),illustrated in FIG. 8, for example, are stored in association with theimaging parameter A=1a.

Then, the score calculation unit 109 reads a first feature amount pairassociated with the imaging parameter A=a1 from the first feature amountpair storage unit 107, calculates a correlation between the featureamounts constituting the pair to thereby calculate a collation score.The distribution generation unit 110 generates a first distribution fromthe calculated collation score of the first feature amount pair andstores it in the distribution storage unit 111 (step S111). Thereby, inthe distribution storage unit 111, a distribution D11 of the firstfeature amount pairs is stored in association with the imagingparameters A=a1 as illustrated in FIG. 9, for example.

Further, the score calculation unit 109 reads a second feature amountpair associated with the imaging parameter A=a1 from the second featureamount pair storage unit 108, calculates a correlation between thefeature amounts constituting the pair to thereby calculate a collationscore. The distribution generation unit 110 generates a seconddistribution from the calculated collation score of the second featureamount pair and stores it in the distribution storage unit 111 (stepS112). Thereby, in the distribution storage unit 111, a distribution D12of the second feature amount pairs is stored in association with theimaging parameters A=a1 as illustrated in FIG. 9, for example.

Then, the imaging parameter determination unit 113 reads the firstdistribution D11 and the second distribution D12 from the distributionstorage unit 111, calculates the degree of separation thereof, andstores it in the distribution storage unit 111. Thereby, in thedistribution storage unit 111, a degree of separation SP1 is stored inassociation with the imaging parameters A=a1 as illustrated in FIG. 9,for example.

Here, the degree of separation SP1 of the two distributions is a measureor an index value indicating how much the two distributions D11 and D12are separated. As the degree of separation, measures provided below asexamples may be used, for example.

Example 1 of Measure of Degree of Separation

As illustrated in FIG. 10, a ratio of inter-class dispersion σ_(b) ² tointra-class dispersion σ_(w) ² is given by the expressions providedbelow, where regarding the distribution of scores of the first featureamount pairs (first distribution), m_(g) represents an average, σ_(g)represents dispersion, and ω_(g) represents the number of pairs thereof,and regarding the distribution of scores of the second feature amountpairs (second distribution), m₁ represents an average, σ_(i) representsdispersion, and ω_(i) represents the number of pairs thereof.

σ_(w) ²=(ω_(g)σ_(g) ²+ω_(i)σ_(i) ²)/(ω_(g)+ω_(i))  (1)

σ_(b) ²=ω_(g)ω_(i)(m _(g) −m _(i) ²)/(ω_(g)+ω_(i))²  (2)

Then, a ratio of inter-class dispersion to intra-class dispersion, givenby the expression provided below, can be used as a measure of the degreeof separation.

Degree of separation=a ratio of inter-class dispersion to intra-classdispersion=σ_(b) ²/σ_(w) ²  (3)

Example 2 of Measure of Degree of Separation

As illustrated in FIG. 11, the ratio of a largest value S_(i) to asmallest value S_(g), given by the expression provided below, can beused as a measure of the degree of separation, where S_(g) represents asmallest value of distribution of scores of the first feature amountpairs (first distribution) and S_(i) represents a largest value ofdistribution of scores of the second feature amount pairs (seconddistribution).

Degree of separation=ratio of largest value of second distribution tosmallest value of first distribution=S _(i) /S _(g)  (4)

Example 3 of Measure of Degree of Separation

An equal error rate (EER) in which a false rejection rate (FRR) obtainedfrom the distribution of scores of the first feature amount pairs and afalse acceptance rate (FAR) obtained from the distribution of scores ofthe second feature amount pairs become equal is used as a measure of thedegree of separation. For example, the FRR can be obtained as acumulative histogram of scores of the first feature amount pairs(normalized by the total number of the first feature amount pairs), asillustrated in FIG. 12A. Also, the FAR can be obtained as a cumulativehistogram of scores of the second feature amount pairs (normalized bythe total number of the second feature amount pairs), as illustrated inFIG. 12A. Furthermore, the EER can be obtained as frequency(probability) of intersection between the EER and FRR, as illustrated inFIG. 12A. Further, when the cumulative histogram of the first scores andthe cumulative histogram of the second scores are completely separated,the EER can be calculated by extrapolation by the cumulativedistribution function that approximates the respective cumulativehistograms, as illustrated in FIG. 12B.

Then, the imaging parameter determination unit 113 compares thecalculated degree of separation SP1 with a predetermined threshold tothereby determine whether or not the first distribution D11 based on thefirst feature amount pairs and the second distribution D12 based on thesecond feature amount pairs are separated from each other by thethreshold or more (step S114). Then, when the degree of separation SP1between the first distribution D11 and the second distribution D12 isequal to or larger than the threshold, the imaging parameterdetermination unit 113 stores the value a1 of the imaging parameter A atthat time in the imaging parameter storage unit 114, and ends theprocess illustrated in FIG. 4.

On the other hand, when the degree of separation SP1 between the firstdistribution D11 and the second distribution 12 is smaller than thethreshold, the imaging parameter determination unit 113 determines thatthe N pieces of sample objects cannot be distinguished from each otherunder the imaging condition of the candidate value a1 of the imagingparameter A at that time. Then, the imaging parameter determination unit113 increments the variable i (step S115) and confirms that i is notlarger than n, and returns to step S102. Thereby, the process similar tothat described above is repeated with the value of the imagingparameters A being fixed to a candidate value a2 (steps S102 to S114).

Thereafter, the process illustrated in FIG. 4 is performed until eitherone of the following conditions is first established: a candidate valueof the imaging parameter A in which the degree of separation between thefirst distribution and the second distribution becomes the threshold orlarger is found, or the variable i becomes larger than n. Note that whena candidate value of the imaging parameter A in which the degree ofseparation becomes the threshold or larger is not found until thevariable i becomes larger than n, the imaging parameter determinationunit 113 outputs an error message (step S118), and ends the processillustrated in FIG. 4.

FIG. 13 is a flowchart illustrating another example of a process ofdetermining the value of a specific imaging parameter A. Compared withthe process illustrated in FIG. 4, the process illustrated in FIG. 13differs from the process of FIG. 4 in that steps S114, S117, and S118are replaced with steps S114A, S117A, and S118A. The other points arethe same as those illustrated in FIG. 4.

At step S114A, the imaging parameter determination unit 113 determineswhether or not the calculated degree of separation is equal to or largerthan the threshold and is equal to or larger than the degree ofseparation of the imaging parameter A stored in the imaging parameterstorage unit 114. When the calculated degree of separation is equal toor larger than the threshold and is equal to or larger than the degreeof separation of the imaging parameter A stored in the imaging parameterstorage unit 114, the imaging parameter determination unit 113overwrites the value and the degree of separation of the imagingparameter A, stored in the imaging parameter storage unit 114, to thecandidate value and the degree of separation of the current imagingparameter A (step S117A). Then, the imaging parameter determination unit113 proceeds to step S115. Meanwhile, when the calculated degree ofseparation is not equal to or larger than the threshold or, even thoughit is equal to or larger than the threshold, when it is not equal to orlarger than the degree of separation of the imaging parameter A storedin the imaging parameter storage unit 114, the imaging parameterdetermination unit 113 skips over step S117A and proceeds to step S115.

Then, when the imaging parameter determination unit 113 determines thatthe variable i becomes larger than n at step S116, the imaging parameterdetermination unit 113 outputs an error message if the value of theimaging parameter A is not stored in the imaging parameter storage unit114 (step S118A), and ends the process of FIG. 13. If the value of theimaging parameter A is stored in the imaging parameter storage unit 114,the value of the stored imaging parameters A becomes the value of theimaging parameters A that is equal to or larger than the threshold andthat gives a largest degree of separation.

[Operation of Individual Registration]

Next, operation of individual registration for registering each objectto be managed will be described.

FIGS. 14 and 15 are a process flow and a flowchart of an operation ofindividual registration. First, the imaging condition control unit 102reads the value of the imaging parameter A determined by the operationof the above-described pre-processing from the imaging parameter storageunit 114, and sets the imaging condition determined by the value of theimaging parameter A to the imaging unit 101 (step S121). For example, inthe case where the imaging unit 101 uses the imaging aid described inPatent Document 1 and the height of the side surface of the coveringportion is stored in the imaging parameter storage unit 114 as the valueof the imaging parameter A, the imaging condition control unit 102performs adjustment so that the height of the side surface of thecovering portion matches the value of the imaging parameter A. Settingof the imaging condition to the imaging unit 101 may be automated or maybe set manually by the user by displaying the imaging condition on thescreen display unit 153.

Next, the imaging unit 101 captures an image of an uneven pattern of oneor more objects that are targets of individual registration at leastonce each under the imaging condition set, and stores in the imagestorage unit 103 (step S122).

Then, the feature amount extraction unit 104 reads the images of theuneven patterns on the surfaces of one or more objects that are targetsof individual registration stored in the image storage unit 103,extracts the feature amount from each of the images, and stores is inthe feature amount storage unit 105 (step S123). At this time, thefeature amount storage unit 105 registers the individual unique featureamount by linking it with (in association with) information related tothe object that is a registration target, such as individual ID numberof the registration target, registration date, size, and productspecification (also referred to as supplementary information). With thisprocess, it is possible to present the individual management informationof the object such as a product, on the basis of the determinationresult of individual identification and individual authenticationdescribed below.

FIG. 16 illustrates an example of contents of the individualregistration information stored in the feature amount storage unit 105.The feature amounts PF1, PF2, . . . , PFn and supplementary informationSI1, SI2, . . . , SIn are feature amounts and supplementary informationcorresponding one-to-one to the individual of a registration target.

[Operation of Individual Identification and Individual Collation]

Next, operation of identifying and collating individual objects to bemanaged will be described.

FIGS. 17 and 18 are a process flow and a flowchart illustrating anoperation for individual identification and individual collation. First,the imaging condition control unit 102 reads the value of the imagingparameter A determined by the operation of the above-describedpre-processing from the imaging parameter storage unit 114, and sets theimaging condition determined by the value of the imaging parameter A tothe imaging unit 101 (step S131). For example, in the case where theimaging unit 101 uses the imaging aid described in Patent Document 1 andthe height of the side surface of the covering portion is stored in theimaging parameter storage unit 114 as the value of the imaging parameterA, the imaging condition control unit 102 performs adjustment so thatthe height of the side surface of the covering portion matches the valueof the imaging parameter A. Setting of the imaging condition to theimaging unit 101 may be automated or may be set manually by the user bydisplaying the imaging condition on the screen display unit 153.

Next, the imaging unit 101 captures an image of an uneven pattern of theobject that is a target of individual identification and collation atleast once under the imaging condition set, and stores it in the imagestorage unit 103 (step S132). Then, the feature amount extraction unit104 reads the image of the uneven pattern on the surface of the objectthat is a target of individual identification and collation stored inthe image storage unit 103, extracts the feature amount from the image,and outputs it to the score calculation unit 109 (step S133).Hereinafter, the feature amount output from the feature amountextraction unit 104 to the score calculation unit 109 at that time isreferred to as a feature amount of an individualidentification/collation object.

Then, the score calculation unit 109 calculates correlation between thefeature amount of the individual identification/collation target and allof the feature amounts PF1 to PFn registered in the feature amountstorage unit 105, and calculates collation scores with respect to all ofthe feature amounts PF1 to PFn (step S134). Then, on the basis of thecollation scores calculated by the score calculation unit 109, thedetermination unit 115 determines the feature amount stored in thefeature amount storage means 105 that matches the feature amount of theindividual identification/collation target. For example, thedetermination unit 115 sorts the collation scores between the featureamount of the individual identification/collation target and all of theregistered feature amount, and selects the feature amount in which thecollation score is the largest (largest correlation) (step S135). Then,the determination unit 115 reads the supplementary information linked tothe selected feature amount from the feature amount storage unit 105,and outputs it as product information of the product that is a target ofidentification and collation.

The determination unit 115 may determine whether or not the collationscores between the feature amount of the individualidentification/collation target and all of the feature amounts stored inthe feature amount storage unit 105 exceed a preset threshold. If noneof the collation scores between the feature amount of the individualidentification/collation target and all of the feature amounts stored inthe feature amount storage unit 105 exceed the threshold, thedetermination unit 115 determines that the product that is a target ofidentification and collation is not registered, and outputs informationrepresenting an authentication error. The determination unit 115 thatoperates as described above can be used for individual authenticationpurpose such as authenticity determination of a management target.

Then, when the information presentation unit 116 receives productinformation or authentication error information from the determinationunit 115, the information presentation unit 116 displays, on a displaydevice not shown, product information and individual authenticationinformation that are individual identification results of the productthat is a target of identification and collation, or outputs them to anexternal terminal (step S136).

Next, a preferred exemplary configuration of the imaging unit 101 willbe described.

Example 1 of Imaging Unit 101

FIG. 19 is a side view of an exemplary configuration of the imaging unit101. The imaging unit 101 of this example includes a camera 201, acamera lens 202 mounted on the camera 201, and a coaxial verticalilluminator 203 of a box type.

The coaxial vertical illuminator 203 of a box type includes a lightsource 204 such as an LED, and a box 207 incorporating a beam splitter206 that irradiates a surface of the object 205 with illumination lightfrom the light source 204 along the optical axis of the camera lens 202.The light reflected at the surface of the object 205 passes through thebeam splitter 206 and is made incident on the camera lens 202, and formsan image. The beam splitter 206 may be a half mirror.

According to the imaging unit 101 of FIG. 19 using the coaxial verticalilluminator 203 of a box type, the surface directly facing the camera,of the pattern formed on the surface of the object 205, can be imaged aswhite (high brightness), as illustrated in FIG. 20. By adjusting thedistance from the coaxial vertical illuminator 203 to the surface of theobject 205, it is possible to adjust the incident angle of light in thecamera confronting direction. That is, as illustrated in FIG. 21A, whenthe distance from the coaxial vertical illuminator 203 to the surface ofthe object 205 is longer, parallelism of the illumination light isimproved because the light does not spread, so that the angle of thewhite (high brightness) in FIG. 20 can be narrowed. On the other hand,as illustrated in FIG. 21B, when the distance from the coaxial verticalilluminator 203 to the surface of the object 205 is shorter, parallelismof the illumination light is lowered because the light spreads, so thatthe angle of the white (high brightness) in FIG. 20 can be widened.Therefore, in the imaging unit 101 using the coaxial verticalilluminator 203, the distance from the coaxial vertical illuminator 203to the surface of the object 205 can be used as a specific imagingparameter A.

Further, by adjusting the size of the illumination of the coaxialvertical illuminator 203, it is possible to adjust the size of thesurface of the corresponding object 205 and the incident angle of thelight in the camera confronting direction. Therefore, in the imagingunit 101 using the coaxial vertical illuminator 203, a combination ofthe distance from the coaxial vertical illuminator 203 to the surface ofthe object 205 and the size of the illumination can be used as aspecific imaging parameter A.

Example 2 of Imaging Unit 101

FIG. 22 is a side view of another exemplary configuration of the imagingunit 101. The imaging unit 101 of this example includes the camera 201,the camera lens 202 mounted on the camera 201, and a coaxial verticalilluminator 210 of a lens-barrel type.

The coaxial vertical illuminator 210 of a lens-barrel type includes thelight source 204 such as an LED, and a lens-barrel 211 incorporating thebeam splitter 206 that irradiates a surface of the object 205 withillumination light from the light source 204 along the optical axis ofthe camera lens 202. The light reflected at the surface of the object205 passes through the beam splitter 206 and is made incident on thecamera lens 202, and forms an image. The beam splitter 206 may be a halfmirror.

According to the coaxial vertical illuminator 210 of the lens-barreltype, the surface directly facing the camera, of the uneven patternformed on the surface of the object 205, can be imaged as white (highbrightness), as illustrated in FIG. 20. By adjusting the distance fromthe coaxial vertical illuminator 210 to the surface of the object 205,it is possible to adjust the incident angle of the light in the cameraconfronting direction. Therefore, in the imaging unit 101 using thecoaxial vertical illuminator 210, the distance from the coaxial verticalilluminator 210 to the surface of the object 205 can be used as aspecific imaging parameter A.

Further, by adjusting the size of the illumination of the coaxialvertical illuminator 210, it is possible to adjust the size of thesurface of the corresponding object 205 and the incident angle of lightin the camera confronting direction. Therefore, in the imaging unit 101using the coaxial vertical illuminator 210, a combination of thedistance from the coaxial vertical illuminator 210 to the surface of theobject 205 and the size of the illumination can be used as a specificimaging parameter A.

Example 3 of Imaging Unit 101

FIG. 23 is a side view of another exemplary configuration of the imagingunit 101. The imaging unit 101 of this example includes the camera 201,the camera lens 202 mounted on the camera 201, and a ring illuminator221.

The ring illuminator 221 is literally a ring-shaped illuminator. Thelight emitted from the ring illuminator 221 and reflected at the surfaceof the object 205 passes through the cavity in the central portion ofthe ring illuminator 221 and is made incident on the camera lens 202,and forms an image.

The ring illuminator 221 can realize illumination similar to coaxialvertical incident when a distance to the surface of the object 205 islong. As a result, the surface directly facing the camera, of the unevenpattern formed on the surface of the object 205, can be imaged as white(high brightness), as illustrated in FIG. 20. On the other hand, in thering illuminator 221, an image having gradation opposite to that in thecoaxial vertical incident is realized when a distance to the surface ofthe object 205 is short. That is, the surface directly facing thecamera, of the uneven pattern formed on the surface of the object 205,can be imaged as black (low brightness), as illustrated in FIG. 24. Byadjusting the distance from the ring illuminator 221 to the surface ofthe object 205, it is possible to adjust the incident angle of light inthe camera confronting direction. Therefore, in the imaging unit 101using the ring illuminator 221, the distance from the ring illuminator221 to the surface of the object 205 can be used as a specific imagingparameter A.

Further, by adjusting the size of the illumination of the ringilluminator 221, it is possible to adjust the size of the surface of thecorresponding object 205 and the incident angle of light in the cameraconfronting direction. Therefore, in the imaging unit 101 using the ringilluminator 221, a combination of the distance from the ring illuminator221 to the surface of the object 205 and the size of the illuminationcan be used as a specific imaging parameter A.

Example 4 of Imaging Unit 101

FIG. 25 is a side view of another exemplary configuration of the imagingunit 101. The imaging unit 101 of this example includes the camera 201,the camera lens 202 mounted on the camera 201, and a dome illuminator231.

The dome illuminator 231 is literally a dome-shaped illuminator. Thelight emitted from the dome illuminator 231 and reflected at the surfaceof the object 205 passes through the cavity in the central portion ofthe dome illuminator 231 and is made incident on the camera lens 202,and forms an image.

According to the dome illuminator 231, the surface directly facing thecamera, of the uneven pattern formed on the surface of the object 205,can be imaged as black (low brightness), as illustrated in FIG. 24. Byadjusting the distance from the dome illuminator 231 to the surface ofthe object 205, it is possible to adjust the incident angle of light inthe camera confronting direction. Therefore, in the imaging unit 101using the dome illuminator 231, the distance from the dome illuminator231 to the surface of the object 205 can be used as a specific imagingparameter A.

Further, by adjusting the size of the illumination of the domeilluminator 231, it is possible to adjust the size of the surface of thecorresponding object 205 and the incident angle of light in the cameraconfronting direction. Therefore, in the imaging unit 101 using the domeilluminator 231, a combination of the distance from the dome illuminator231 to the surface of the object 205 and the size of the illuminationcan be used as a specific imaging parameter A.

As described above, according to the present embodiment, it is possibleto determine the imaging condition useful for identifying three or moretypes of objects that are similar to one another. This is because thepresent embodiment includes the imaging unit 101 that acquires aplurality of images by imaging each of N (N≥3) types of objects aplurality of times by setting the value of a specific imaging parameterA, among a plurality of types of imaging parameters, as a certaincandidate value and changing the value of the remaining imagingparameter B, the feature amount extraction unit 104 that extracts afeature amount from each of the plurality of the images, the featureamount pair generation unit 106 that generates, as a first featureamount pair for each of the N types of objects, a feature amount pair inwhich two feature amounts constituting the feature amount pair areextracted from a plurality of images of objects of the same type, andgenerates, as a second feature amount pair for every combination of theN types of objects, a feature amount pair in which two feature amountsconstituting the feature amount pair are extracted from a plurality ofimages of objects of the different types, the distribution generationunit 110 that generates a first distribution that is a distribution ofcollation scores of the first feature amount pairs and generates asecond distribution that is a distribution of collation scores of thesecond feature amount pairs, and the imaging parameter determinationunit 113 that determines the propriety of adopting the candidate valueon the basis of the degree of separation between the first distributionand the second distribution.

Further, according to the present embodiment, identification andcollation of collation target objects are performed by using an imagingcondition useful for identifying three or more types of objects that aresimilar to one another. Therefore, there is no need to capture images ofthe collation target objects by changing the imaging condition foridentification and collation as described in Patent Document 2.Therefore, it is possible to efficiently perform individualidentification and individual collation.

Modification 1 of Present Embodiment

In the above description, two feature amounts constituting the firstfeature amount pair are feature amounts extracted from a plurality ofimages captured on the physically same object. Moreover, in the abovedescription, two feature amounts constituting the second feature amountpair are feature amounts extracted from a plurality of images capturedon physically different objects. In contrast, as a modification of thepresent embodiment, two feature amounts constituting the first featureamount pair may be feature amounts extracted from a plurality of imagescaptured on the physically same object or physically different objectsmanufactured on the same production line or by the same manufacturingmold, and two feature amounts constituting the second feature amountpair may be feature amounts extracted from a plurality of imagescaptured on physically different objects manufactured on differentproduction lines or by different manufacturing molds.

Here, the manufacturing mold means a mold or cutting equipment used tomanufacture products by casting, forging, cutting or the like. Further,the production line means a process of manufacturing products using oneor more manufacturing molds in an assembly line.

For example, it is assumed that products are mass-produced by casting orforging with use of a manufacturing mold X1, and in parallel with it,products are mass-produced by casting or forging with use of amanufacturing mold X2 that is the same as the manufacturing mold X1. Inthat case, on the products manufactured by the manufacturing mold X1, apattern unique to the manufacturing mold X1 is transferred to the entiresurface. Also, on the products manufactured by the manufacturing moldX2, a pattern unique to the manufacturing mold X2 is transferred to theentire surface.

Further, it is also assumed that products are mass-produced by cutting amaterial with use of a cutting device Y1, and in parallel with it,products are mass-produced by cutting a material with use of a cuttingdevice Y2 that is the same as the cutting device Y1, for example. Inthat case, in the products manufactured by the cutting device Y1, fineirregularities in surface roughness, that are unique to the blade usedfor cutting of the cutting device Y1, appear on the cut surface. Also,in the products manufactured by the cutting device Y2, fineirregularities in surface roughness, that are unique to the blade usedfor cutting of the cutting device Y2, appear on the cut surface. Thesame machining method and the devices mentioned here are only examples.Other same manufacturing steps and devices may also be handledsimilarly.

According to the present modification, it is possible to determine theimaging condition useful for identifying the production line or themanufacturing mold used for manufacturing the object. Further, thefeature amount extracted from an image obtained by capturing an objectunder the determined imaging condition is unique to the production lineor the manufacturing mold used for manufacturing the object.Accordingly, by using such a feature amount for identification andcollation, it is possible to perform identification and collation of aproduct to know the production line or the manufacturing mold used formanufacturing the product.

Second Exemplary Embodiment

Referring to FIG. 26, an individual identifying device 300 according toa second exemplary embodiment of the present invention acquires an imageof an uneven pattern 311 formed on a surface of an object 310 for eachfeed pitch on the upper surface of a conveyor belt 305. The object 310is a metal part manufactured on a manufacturing line for metal latheprocessing, or the like, for example, and has a fine uneven pattern 311that is unique to the object and is formed on a head surface of theobject 310. The conveyor belt 305 is also referred to as a conveyancepath.

The individual identifying device 300 includes an imager 302, a heightadjuster 303, a controller 304, a conveyor belt drive 306, and anoptical switch 308. The imager 302 is disposed above the conveyor belt305. The height of the imager 302, that is, the distance from the imager302 to the object 310 on the conveyor belt 305 immediately below it isadjustable by the height adjuster 303.

The conveyor belt drive 306 is configured of a stepping motor forpitch-feeding the conveyor belt 305, or the like. The optical switch 208is a sensor that detects whether or not the object 310 is present on theconveyor belt 305 immediately below the imager 302.

The imager 302 is a unit that acquires an image of the uneven pattern311 on the surface of the object 310 on the conveyor belt 305 positionedimmediately below the imager 302. The imager 302 may be configured ofthe camera 201, the camera lens 202, and the coaxial verticalilluminator 203 of a box type as illustrated in FIG. 19. Alternatively,the imager 302 may be configured of the camera 201, the camera lens 202,and the coaxial vertical illuminator 210 of a mirror-barrel type asillustrated in FIG. 22. Alternatively, the imager 302 may be configuredof the camera 201, the camera lens 202, and the ring illuminator 221 asillustrated in FIG. 23. Alternatively, the imager 302 may be configuredof the camera 201, the camera lens 202, and the dome illuminator 231 asillustrated in FIG. 25.

The controller 304 is a unit that controls the entire individualidentifying device 300. The controller 304 is connected with the imager302, the height adjuster 303, the conveyor belt drive 306, and theoptical switch 308 in a wired or wireless manner, and transmits acommand thereto to thereby control the operation thereof, or receives asignal therefrom. The controller 304 has respective functions of theimaging condition control unit 102, the image storage unit 103, thefeature amount extraction unit 104, the feature amount storage unit 105,the score calculation unit 109, the determination unit 115, theinformation presentation unit 116, and the imaging parameter storageunit 114 in FIG. 1 of the first exemplary embodiment.

Next, operation of the individual identifying device 300 according tothe present embodiment will be described with reference to the drawings.Operation of the individual identifying device 300 is roughly dividedinto two as described below.

(b) Operation of individual registration(c) Operation of individual identification and individual collation

[Operation of Individual Registration]

First, operation of individual registration for registering each objectto be managed will be described. When the operation of individualregistration is performed, on the conveyor belt 305, the objects 310that are targets of individual registration are placed at predeterminedintervals on the conveyor belt 305.

FIG. 27 is a flowchart of an operation of individual registration.First, the controller 304 sets an imaging condition determined by thevalue of the imaging parameter A stored in the storage unit incorporatedtherein, to the imager 302 (step S201). Here, the value of the imagingparameter A is determined in advance by an operation similar to theoperation of pre-processing in the first exemplary embodiment, and isstored in the storage unit of the controller 304. That is, the storedvalue of the imaging parameter A is a value useful for identifying threeor more types of objects that are similar to one another.

For example, when the imager 302 is an imager that uses the coaxialvertical illuminator 203 of a box type as illustrated in FIG. 19, thecontroller 304 adjusts the height of the imager 302 by the heightadjuster 303 such that the distance from the coaxial verticalilluminator 203 to the surface of the object 310 matches the value ofthe imaging parameter A.

Then, the controller 304 issues a command to the conveyor belt drive 306to drive the conveyor belt 305 by one pitch (S202). Then, the controller304 detects whether or not the object 310 is positioned immediatelybelow the imager 302 on the basis of a signal received from the opticalswitch 308 (step S203). When the object 310 is not positionedimmediately below the imager 302, the controller 304 returns to stepS202 and repeats the same operation as described above.

On the other hand, when the object 310 is positioned immediately belowthe imaging unit 302, the controller 304 issues a command to the imager302 to acquire an image of the uneven pattern on the surface of theobject 310 (step S204). Then, the controller 304 extracts a featureamount for individual identification from the acquired image of theuneven pattern (step S205). Then, the controller 304 registers theextracted feature amount in the storage unit incorporated therein, inassociation with supplementary information of the object 310 (stepS206). Then, the controller 304 returns to step S202, and repeats thesame operation as described above.

[Operation of Individual Identification and Individual Collation]

Next, operation of identifying and collating each object to be managedwill be described. When the operation of individual identification andindividual collation is performed, the objects 310 that are targets ofindividual identification and individual collation are placed atpredetermined intervals on the conveyor belt 305.

FIG. 28 is a flowchart of an operation of individual identification andindividual collation. First, the controller 304 sets an imagingcondition determined by the value of the imaging parameter A stored inthe storage unit incorporated therein, to the imager 302 (step S211).Then, the controller 304 issues a command to the conveyor belt drive 306to drive the conveyor belt 305 by one pitch (S212). Then, the controller304 detects whether or not the object 310 is positioned immediatelybelow the imager 302, on the basis of a signal received from the opticalswitch 308 (step S213). Then, when the object 310 is not positionedimmediately below the imager 302, the controller 304 returns to stepS212 and repeats the same operation as described above. On the otherhand, when the object 310 is positioned immediately below the imager302, the controller 304 issues a command to the imager 302 to acquire animage of the uneven pattern on the surface of the object 310 (stepS214). Next, the controller 304 extracts a feature amount for individualidentification from the acquired image of the uneven pattern (stepS215). The operation of steps S211 to S215 described above are the sameas steps S201 to S205 of the operation of individual registration.

Next, the controller 304 calculates correlations between the featureamount of the target of individual identification/collation extracted atstep S215 and all of the feature amounts registered in the storage unitincorporated therein, and calculates collation scores with respect toall of the registered feature amounts (step S216). Then, the controller304 sorts the collation scores between the feature amount of the targetof individual identification/collation and all of the registered featureamounts, and selects a feature amount in which the collation score isthe largest (highest correlation) (step S217). Then, controller 304reads the supplementary information linked to the selected featureamount from the storage unit, and outputs it as product information ofthe product that is a target of identification and collation (stepS218).

The controller 304 may determine whether or not the collation scoresbetween the feature amount of the target of individualidentification/collation and all of the registered feature amountsexceed a preset threshold. If none of the collation scores between thefeature amount of the target of individual identification/collation andall of the registered feature amounts exceed the threshold, thecontroller 304 determines that the product that is a target ofidentification and collation is not registered, and outputs informationrepresenting an authentication error. The controller 304 that operatesas described above can be used for individual authentication purposesuch as authenticity determination of a management target.

Third Exemplary Embodiment

Referring to FIG. 29, an individual identifying device 400 according toa third exemplary embodiment of the present invention includes animaging unit 401, an extraction unit 402, a feature amount pairgeneration unit 403, and an imaging parameter adjustment unit 404.

The imaging unit 401 has a function of acquiring a plurality of imagesobtained by imaging each of the N (N≥3) types of objects a plurality oftimes by setting the value of a specific imaging parameter, among aplurality of types of imaging parameters, as a certain candidate valueand changing the value of the remaining imaging parameter. The imagingunit 401 may have a configuration similar to that of the imaging unit101 and the imaging condition control unit 102 of FIG. 1, for example.However, it is not limited thereto. The extraction unit 402 has afunction of extracting a feature amount from each of the images acquiredby the imaging unit 401. The extraction unit 402 may have aconfiguration similar to that of the feature amount extraction unit 104of FIG. 1, for example. However, it is not limited thereto. The featureamount pair generation unit 403 has a function of generating, as a firstfeature amount pair for each of N types of objects, a feature amountpair in which two feature amounts constituting the pair are extractedfrom a plurality of images of objects of the same type, and generating,as a second feature amount pair for every combination of N types ofobjects, a feature amount pair in which two feature amounts constitutingthe pair are extracted from a plurality of images of objects ofdifferent types. The feature amount pair generation unit 403 may have aconfiguration similar to that of the feature amount pair generation unit106 of FIG. 1, for example. However, it is not limited thereto. Theimaging parameter adjustment unit 404 has a function of generating afirst distribution that is a distribution of collation scores of thefirst feature amount pairs, generating a second distribution that is adistribution of collation scores of the second feature amount pairs, andon the basis of the degree of separation between the first distributionand the second degree, determining the propriety of adopting thecandidate value. The imaging parameter adjustment unit 404 may have aconfiguration similar to that of the imaging parameter determinationunit 113 of FIG. 1, for example. However, it is not limited thereto.

FIG. 30 is a flowchart illustrating an operation of the individualidentifying device 400 according to the present embodiment. Hereinafter,the individual identifying device 400 according to the presentembodiment will be described with reference to FIG. 30.

First, the imaging unit 401 acquires a plurality of images obtained byimaging each of the N (N≥3) types of objects a plurality of times bysetting the value of a specific imaging parameter, among a plurality oftypes of imaging parameters, as a certain candidate value and changingthe value of the remaining imaging parameter (step S401). Then, theextraction unit 402 extracts a feature amount from each of the imagesacquired by the imaging unit 401 (step S402). Then, the feature amountpair generation unit 403 generates, as a first feature amount pair foreach of the N types of objects, a feature amount pair in which twofeature amounts constituting the pair are extracted from a plurality ofimages of objects of the same type, and generates, as a second featureamount pair for every combination of the N types of objects, a featureamount pair in which two feature amounts constituting the pair areextracted from a plurality of images of objects of different types (stepS403). Then, the imaging parameter adjustment unit 404 generates a firstdistribution that is a distribution of collation scores of the firstfeature amount pairs, generates a second distribution that is adistribution of collation scores of the second feature amount pairs, andon the basis of the degree of separation between the first distributionand the second degree, determines the propriety of adopting thecandidate value (step S404).

As described above, according to the present embodiment, it is possibleto determine the imaging condition useful for identifying three or moretypes of objects that are similar to one another. This is because thepresent embodiment includes the imaging unit 401, the extraction unit402, the feature amount pair generation unit 403, and the imagingparameter adjustment unit 404 that functions as described above.

Fourth Exemplary Embodiment

Referring to FIG. 31, an individual registration device 500 according toa fourth exemplary embodiment of the present invention includes animaging parameter storage unit 501, an imaging condition control unit502, an imaging unit 503, and an extraction unit 504.

The imaging parameter storage unit 501 has a function of storing valuesof imaging parameters. The imaging parameter storage unit 501 stores thevalue of the imaging parameter A determined to be adopted by theindividual identifying device of FIG. 1, for example. That is, thestored value of the imaging parameter A is a value useful foridentifying three or more types of objects that are similar to oneanother. The imaging condition control unit 502 has a function ofsetting an imaging condition determined by the value of the imagingparameter stored in the imaging parameter storage unit 501. The imagingcondition control unit 502 may have a configuration similar to that ofthe imaging condition control unit 102 of FIG. 1, for example. However,it is not limited thereto. The imaging unit 503 has a function ofacquiring an image of an uneven pattern on a surface of an object notillustrated, under the imaging condition set. The imaging unit 503 mayhave a configuration similar to that of the imaging unit 101 of FIG. 1,for example. However, it is not limited thereto. The extraction unit 504has a function of extracting a feature amount from an image acquired bythe imaging unit 503 and registering it in a storage unit notillustrated. The extraction unit 504 may have a configuration similar tothat of the feature amount extraction unit 104 of FIG. 1, for example.However, it is not limited thereto.

FIG. 32 is a flowchart illustrating an operation of the individualregistration device 500 according to the present embodiment.Hereinafter, the individual registration device 500 according to thepresent embodiment will be described with reference to FIG. 32.

First, the imaging condition control unit 502 sets an imaging conditiondetermined by the value of an imaging parameter stored in the imagingparameter storage unit 501 (step S501). Then, the imaging unit 503acquires an image of an uneven pattern on a surface of an object, underthe imaging condition set (step S502). Then, the extraction unit 504extracts a feature amount from an image acquired by the imaging unit 503and registers it in a storage unit not illustrated (step S503).

As described above, according to the present embodiment, it is possibleto acquire images of objects under the imaging condition useful foridentifying the objects of three or more types that are similar to oneanother, extract features from the images, and register them in thestorage unit.

Fifth Exemplary Embodiment

Referring to FIG. 33, an individual collation device 600 according to afifth exemplary embodiment of the present invention includes an imagingparameter storage unit 601, an imaging condition control unit 602, animaging unit 603, an extraction unit 604, and a collation unit 605.

The imaging parameter storage unit 601 has a function of storing valuesof imaging parameters. The imaging parameter storage unit 601 stores thevalue of the imaging parameter A determined to be adopted by theindividual identifying device of FIG. 1, for example. That is, thestored value of the imaging parameter A is a value useful foridentifying three or more types of objects that are similar to oneanother. The imaging condition control unit 602 has a function ofsetting an imaging condition determined by the value of the imagingparameter stored in the imaging parameter storage unit 601. The imagingcondition control unit 602 may have a configuration similar to that ofthe imaging condition control unit 102 of FIG. 1, for example. However,it is not limited thereto. The imaging unit 603 has a function ofacquiring an image of an uneven pattern on a surface of an object notillustrated, under the imaging condition set. The imaging unit 603 mayhave a configuration similar to that of the imaging unit 101 of FIG. 1,for example. However, it is not limited thereto. The extraction unit 604has a function of extracting a feature amount from an image acquired bythe imaging unit 603. The extraction unit 604 may have a configurationsimilar to that of the feature amount extraction unit 104 of FIG. 1, forexample. However, it is not limited thereto. The collation unit 605 hasa function of collating a feature amount extracted by the extractionunit 604 with a registered feature amount stored in a storage unit notillustrated. The collation unit 605 may have a configuration similar tothat of the score calculation unit 109 and the determination unit 115 ofFIG. 1, for example. However, it is not limited thereto.

FIG. 34 is a flowchart illustrating an operation of the individualcollation device 600 according to the present embodiment. Hereinafter,the individual collation device 600 according to the present embodimentwill be described with reference to FIG. 34.

First, the imaging condition control unit 602 sets an imaging conditiondetermined by the value of an imaging parameter stored in the imagingparameter storage unit 601 (step S601). Then, the imaging unit 603acquires an image of an uneven pattern on a surface of an object, underthe imaging condition set (step S602). Then, the extraction unit 604extracts a feature amount from the image acquired by the imaging unit603 (step S603). Then, the collation unit 605 collates the featureamount extracted by the extraction unit 604 with a registered featureamount stored in a storage unit not illustrated (step S604).

As described above, according to the present embodiment, it is possibleto acquire an image of an object under the imaging condition useful foridentifying objects of three or more types that are similar to oneanother, extract a feature amount from the image, and collate it withthe feature amount registered in the storage unit.

While the present invention has been described with reference to theexemplary embodiments described above, the present invention is notlimited to the above-described embodiments. The form and details of thepresent invention can be changed within the scope of the presentinvention in various manners that can be understood by those skilled inthe art.

INDUSTRIAL APPLICABILITY

The present invention is applicable to a field of performing individualidentification and management of individual products by acquiring adifference between spontaneous fine patterns generated in the samemanufacturing process, such as fine unevenness and patterns on theproduct surface or random patterns or the like on the material surface,as an image with use of an imaging device such as a camera, andrecognizing the fine pattern. For example, the present invention isapplicable to management of traceability in the manufacturing lines in afactory or the like and traceability with use of fasteners or the likeof brand-named products.

REFERENCE SIGNS LIST

-   100 individual identifying device-   101 imaging unit-   102 imaging condition control unit-   103 image storage unit-   104 feature amount extraction unit-   105 feature amount storage unit-   106 feature amount pair generation unit-   107 first feature amount pair storage unit-   108 second feature amount pair storage unit-   109 score calculation unit-   110 distribution generation unit-   111 distribution storage unit-   113 imaging parameter determination unit-   114 imaging parameter storage unit-   115 determination unit-   116 information presentation unit-   150 information processing device-   151 photographing unit-   152 operation input unit-   153 screen display unit-   154 communication interface unit-   155 storage unit-   156 arithmetic processing unit-   157 program-   201 camera-   202 camera lens-   203 coaxial vertical illuminator-   204 light source-   205 object-   206 beam splitter-   207 box-   210 coaxial vertical illuminator-   221 ring illuminator-   231 dome illuminator-   300 individual identifying device-   302 imager-   303 height adjuster-   304 controller-   305 conveyor belt-   306 conveyor belt drive-   308 light switch-   310 object-   311 uneven pattern-   400 individual identifying device-   401 imaging unit-   402 extraction unit-   403 feature amount pair generation unit-   404 imaging parameter adjustment unit-   500 individual registration device-   501 imaging parameter storage unit-   502 imaging condition control unit-   503 imaging unit-   504 extraction unit-   600 individual collation device-   601 imaging parameter storage unit-   602 imaging condition control unit-   603 imaging unit-   604 extraction unit-   605 collation unit

1. An individual identifying device comprising: an imaging unit thatacquires a plurality of images obtained by imaging each of N (N≥3) typesof objects a plurality of times by setting a value of a specific imagingparameter, among a plurality of types of imaging parameters, as acertain candidate value and changing a value of a remaining imagingparameter; an extraction unit that extracts a feature amount from eachof the plurality of the images; a feature amount pair generation unitthat generates, as a first feature amount pair for each of the N typesof objects, a feature amount pair in which two feature amountsconstituting the feature amount pair are extracted from a plurality ofimages of objects of a same type, and generates, as a second featureamount pair for every combination of the N types of objects, a featureamount pair in which two feature amounts constituting the feature amountpair are extracted from a plurality of images of objects of differenttypes; and an imaging parameter adjustment unit that generates a firstdistribution that is a distribution of collation scores of a pluralityof the first feature amount pairs, generates a second distribution thatis a distribution of collation scores of a plurality of the secondfeature amount pairs, and on a basis of a degree of separation betweenthe first distribution and the second distribution, determines proprietyof adopting the candidate value.
 2. The individual identifying deviceaccording to claim 1, wherein the imaging parameter adjustment unit isconfigured to, after changing the value of the specific imagingparameter to another candidate value different from the candidate value,repeatedly perform the acquisition by the imaging unit, the extractionby the extraction unit, the generation by the feature amount pairgeneration unit, and the determination by the imaging parameteradjustment unit.
 3. The individual identifying device according to claim2, wherein the imaging parameter adjustment unit is configured toselect, from among a plurality of the candidate values, one candidatevalue in which the degree of separation between the first distributionand the second distribution becomes equal to or larger than a presetthreshold.
 4. The individual identifying device according to claim 2,wherein the imaging parameter adjustment unit is configured to select,from among a plurality of the candidate values, a candidate value inwhich the degree of separation between the first distribution and thesecond distribution becomes highest.
 5. The individual identifyingdevice according to claim 1, wherein the imaging unit includes a coaxialvertical illuminator that irradiates a surface of the object withillumination light from a light source along an optical axis of a cameralens, and uses a distance from the coaxial vertical illuminator to thesurface of the object as the specific imaging parameter.
 6. Theindividual identifying device according to claim 1, wherein the imagingunit includes a ring illuminator that illuminates a surface of theobject, and uses a distance from the ring illuminator to the surface ofthe object as the specific imaging parameter.
 7. The individualidentifying device according to claim 1, wherein the imaging unitincludes a dome illuminator that illuminates a surface of the object,and uses a distance from the dome illuminator to the surface of theobject as the specific imaging parameter.
 8. The individual identifyingdevice according to claim 1, wherein the extraction unit registers thefeature amount extracted from an image of the object in a storage unitin association with supplementary information of the object.
 9. Theindividual identifying device according to claim 8, further comprising acollation unit that collates the feature amount extracted from the imageof the object by the extraction unit with the feature amount stored inthe storage unit. 10-11. (canceled)
 12. An individual identifying methodcomprising: acquiring a plurality of images obtained by imaging each ofN (N≥3) types of objects a plurality of times by setting a value of aspecific imaging parameter, among a plurality of types of imagingparameters, as a certain candidate value and changing a value of aremaining imaging parameter; extracting a feature amount from each ofthe plurality of the images; generating, as a first feature amount pairfor each of the N types of objects, a feature amount pair in which twofeature amounts constituting the feature amount pair are extracted froma plurality of images of objects of a same type, and generating, as asecond feature amount pair for every combination of the N types ofobjects, a feature amount pair in which two feature amounts constitutingthe feature amount pair are extracted from a plurality of images ofobjects of different types; and generating a first distribution that isa distribution of collation scores of a plurality of the first featureamount pairs, generating a second distribution that is a distribution ofcollation scores of a plurality of the second feature amount pairs, andon a basis of a degree of separation between the first distribution andthe second distribution, determining propriety of adopting the candidatevalue. 13-18. (canceled)
 19. A non-transitory computer-readable mediumstoring a program comprising instructions for causing a computer tofunction as: an imaging unit that acquires a plurality of imagesobtained by imaging each of N (N≥3) types of objects a plurality oftimes by setting a value of a specific imaging parameter, among aplurality of types of imaging parameters, as a certain candidate valueand changing a value of a remaining imaging parameter; an extractionunit that extracts a feature amount from each of the plurality of theimages; a feature amount pair generation unit that generates, as a firstfeature amount pair for each of the N types of objects, a feature amountpair in which two feature amounts constituting the feature amount pairare extracted from a plurality of images of objects of a same type, andgenerates, as a second feature amount pair for every combination of theN types of objects, a feature amount pair in which two feature amountsconstituting the feature amount pair are extracted from a plurality ofimages of objects of different types; and an imaging parameteradjustment unit that generates a first distribution that is adistribution of collation scores of a plurality of the first featureamount pairs, generates a second distribution that is a distribution ofcollation scores of a plurality of the second feature amount pairs, andon a basis of a degree of separation between the first distribution andthe second distribution, determines propriety of adopting the candidatevalue.